Linear.Quaternion:$cexp from linear-1.19.1.3

Percentage Accurate: 99.8% → 99.8%

Time: 5.5s

Alternatives: 7

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

double code(double x, double y) {
	return x * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return x * (Math.sin(y) / y);
}

Python

def code(x, y):
	return x * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(x * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(x * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

double code(double x, double y) {
	return x * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return x * (Math.sin(y) / y);
}

Python

def code(x, y):
	return x * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(x * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(x * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

double code(double x, double y) {
	return x * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return x * (Math.sin(y) / y);
}

Python

def code(x, y):
	return x * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(x * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(x * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]

2. Final simplification 99.8%

   \[\leadsto x \cdot \frac{\sin y}{y} \]

Alternative 2: 57.1% accurate, 9.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 31.5:\\ \;\;\;\;x \cdot \left(1 + -0.16666666666666666 \cdot \left(y \cdot y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \frac{6}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 31.5)
   (* x (+ 1.0 (* -0.16666666666666666 (* y y))))
   (* (/ x y) (/ 6.0 y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 31.5) {
		tmp = x * (1.0 + (-0.16666666666666666 * (y * y)));
	} else {
		tmp = (x / y) * (6.0 / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 31.5d0) then
        tmp = x * (1.0d0 + ((-0.16666666666666666d0) * (y * y)))
    else
        tmp = (x / y) * (6.0d0 / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 31.5) {
		tmp = x * (1.0 + (-0.16666666666666666 * (y * y)));
	} else {
		tmp = (x / y) * (6.0 / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 31.5:
		tmp = x * (1.0 + (-0.16666666666666666 * (y * y)))
	else:
		tmp = (x / y) * (6.0 / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 31.5)
		tmp = Float64(x * Float64(1.0 + Float64(-0.16666666666666666 * Float64(y * y))));
	else
		tmp = Float64(Float64(x / y) * Float64(6.0 / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 31.5)
		tmp = x * (1.0 + (-0.16666666666666666 * (y * y)));
	else
		tmp = (x / y) * (6.0 / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 31.5], N[(x * N[(1.0 + N[(-0.16666666666666666 * N[(y * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(x / y), $MachinePrecision] * N[(6.0 / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 31.5

   1. Initial program 99.9%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 67.8%

      \[\leadsto x \cdot \color{blue}{\left(1 + -0.16666666666666666 \cdot {y}^{2}\right)} \]
   3. Step-by-step derivation

      1. unpow2 67.8%

         \[\leadsto x \cdot \left(1 + -0.16666666666666666 \cdot \color{blue}{\left(y \cdot y\right)}\right) \]

   4. Simplified 67.8%

      \[\leadsto x \cdot \color{blue}{\left(1 + -0.16666666666666666 \cdot \left(y \cdot y\right)\right)} \]

   if 31.5 < y

   1. Initial program 99.6%

      \[x \cdot \frac{\sin y}{y} \]
   2. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

      2. un-div-inv 99.6%

         \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   3. Applied egg-rr 99.6%

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   4. Taylor expanded in y around 0 27.0%

      \[\leadsto \frac{x}{\color{blue}{1 + 0.16666666666666666 \cdot {y}^{2}}} \]
   5. Step-by-step derivation

      1. *-commutative 27.0%

         \[\leadsto \frac{x}{1 + \color{blue}{{y}^{2} \cdot 0.16666666666666666}} \]

      2. unpow2 27.0%

         \[\leadsto \frac{x}{1 + \color{blue}{\left(y \cdot y\right)} \cdot 0.16666666666666666} \]

   6. Simplified 27.0%

      \[\leadsto \frac{x}{\color{blue}{1 + \left(y \cdot y\right) \cdot 0.16666666666666666}} \]

   7. Taylor expanded in y around inf 27.0%

      \[\leadsto \color{blue}{6 \cdot \frac{x}{{y}^{2}}} \]
   8. Step-by-step derivation

      1. unpow2 27.0%

         \[\leadsto 6 \cdot \frac{x}{\color{blue}{y \cdot y}} \]

   9. Simplified 27.0%

      \[\leadsto \color{blue}{6 \cdot \frac{x}{y \cdot y}} \]

   10. Taylor expanded in x around 0 27.0%

       \[\leadsto \color{blue}{6 \cdot \frac{x}{{y}^{2}}} \]
   11. Step-by-step derivation

       1. unpow2 27.0%

          \[\leadsto 6 \cdot \frac{x}{\color{blue}{y \cdot y}} \]

       2. associate-*r/ 27.0%

          \[\leadsto \color{blue}{\frac{6 \cdot x}{y \cdot y}} \]

       3. *-commutative 27.0%

          \[\leadsto \frac{\color{blue}{x \cdot 6}}{y \cdot y} \]

       4. times-frac 27.0%

          \[\leadsto \color{blue}{\frac{x}{y} \cdot \frac{6}{y}} \]

   12. Simplified 27.0%

       \[\leadsto \color{blue}{\frac{x}{y} \cdot \frac{6}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 59.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 31.5:\\ \;\;\;\;x \cdot \left(1 + -0.16666666666666666 \cdot \left(y \cdot y\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \frac{6}{y}\\ \end{array} \]

Alternative 3: 57.3% accurate, 11.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.5:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \frac{x}{y \cdot y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 2.5) x (* 6.0 (/ x (* y y)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 2.5) {
		tmp = x;
	} else {
		tmp = 6.0 * (x / (y * y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 2.5d0) then
        tmp = x
    else
        tmp = 6.0d0 * (x / (y * y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 2.5) {
		tmp = x;
	} else {
		tmp = 6.0 * (x / (y * y));
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 2.5:
		tmp = x
	else:
		tmp = 6.0 * (x / (y * y))
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 2.5)
		tmp = x;
	else
		tmp = Float64(6.0 * Float64(x / Float64(y * y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 2.5)
		tmp = x;
	else
		tmp = 6.0 * (x / (y * y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 2.5], x, N[(6.0 * N[(x / N[(y * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 2.5

   1. Initial program 99.9%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 68.4%

      \[\leadsto \color{blue}{x} \]

   if 2.5 < y

   1. Initial program 99.6%

      \[x \cdot \frac{\sin y}{y} \]
   2. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

      2. un-div-inv 99.6%

         \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   3. Applied egg-rr 99.6%

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   4. Taylor expanded in y around 0 27.0%

      \[\leadsto \frac{x}{\color{blue}{1 + 0.16666666666666666 \cdot {y}^{2}}} \]
   5. Step-by-step derivation

      1. *-commutative 27.0%

         \[\leadsto \frac{x}{1 + \color{blue}{{y}^{2} \cdot 0.16666666666666666}} \]

      2. unpow2 27.0%

         \[\leadsto \frac{x}{1 + \color{blue}{\left(y \cdot y\right)} \cdot 0.16666666666666666} \]

   6. Simplified 27.0%

      \[\leadsto \frac{x}{\color{blue}{1 + \left(y \cdot y\right) \cdot 0.16666666666666666}} \]

   7. Taylor expanded in y around inf 27.0%

      \[\leadsto \color{blue}{6 \cdot \frac{x}{{y}^{2}}} \]
   8. Step-by-step derivation

      1. unpow2 27.0%

         \[\leadsto 6 \cdot \frac{x}{\color{blue}{y \cdot y}} \]

   9. Simplified 27.0%

      \[\leadsto \color{blue}{6 \cdot \frac{x}{y \cdot y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 59.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 2.5:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;6 \cdot \frac{x}{y \cdot y}\\ \end{array} \]

Alternative 4: 57.3% accurate, 11.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 2.5:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \frac{6}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 2.5) x (* (/ x y) (/ 6.0 y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 2.5) {
		tmp = x;
	} else {
		tmp = (x / y) * (6.0 / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 2.5d0) then
        tmp = x
    else
        tmp = (x / y) * (6.0d0 / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 2.5) {
		tmp = x;
	} else {
		tmp = (x / y) * (6.0 / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 2.5:
		tmp = x
	else:
		tmp = (x / y) * (6.0 / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 2.5)
		tmp = x;
	else
		tmp = Float64(Float64(x / y) * Float64(6.0 / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 2.5)
		tmp = x;
	else
		tmp = (x / y) * (6.0 / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 2.5], x, N[(N[(x / y), $MachinePrecision] * N[(6.0 / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 2.5

   1. Initial program 99.9%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 68.4%

      \[\leadsto \color{blue}{x} \]

   if 2.5 < y

   1. Initial program 99.6%

      \[x \cdot \frac{\sin y}{y} \]
   2. Step-by-step derivation

      1. clear-num 99.5%

         \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

      2. un-div-inv 99.6%

         \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   3. Applied egg-rr 99.6%

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   4. Taylor expanded in y around 0 27.0%

      \[\leadsto \frac{x}{\color{blue}{1 + 0.16666666666666666 \cdot {y}^{2}}} \]
   5. Step-by-step derivation

      1. *-commutative 27.0%

         \[\leadsto \frac{x}{1 + \color{blue}{{y}^{2} \cdot 0.16666666666666666}} \]

      2. unpow2 27.0%

         \[\leadsto \frac{x}{1 + \color{blue}{\left(y \cdot y\right)} \cdot 0.16666666666666666} \]

   6. Simplified 27.0%

      \[\leadsto \frac{x}{\color{blue}{1 + \left(y \cdot y\right) \cdot 0.16666666666666666}} \]

   7. Taylor expanded in y around inf 27.0%

      \[\leadsto \color{blue}{6 \cdot \frac{x}{{y}^{2}}} \]
   8. Step-by-step derivation

      1. unpow2 27.0%

         \[\leadsto 6 \cdot \frac{x}{\color{blue}{y \cdot y}} \]

   9. Simplified 27.0%

      \[\leadsto \color{blue}{6 \cdot \frac{x}{y \cdot y}} \]

   10. Taylor expanded in x around 0 27.0%

       \[\leadsto \color{blue}{6 \cdot \frac{x}{{y}^{2}}} \]
   11. Step-by-step derivation

       1. unpow2 27.0%

          \[\leadsto 6 \cdot \frac{x}{\color{blue}{y \cdot y}} \]

       2. associate-*r/ 27.0%

          \[\leadsto \color{blue}{\frac{6 \cdot x}{y \cdot y}} \]

       3. *-commutative 27.0%

          \[\leadsto \frac{\color{blue}{x \cdot 6}}{y \cdot y} \]

       4. times-frac 27.0%

          \[\leadsto \color{blue}{\frac{x}{y} \cdot \frac{6}{y}} \]

   12. Simplified 27.0%

       \[\leadsto \color{blue}{\frac{x}{y} \cdot \frac{6}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 59.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 2.5:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} \cdot \frac{6}{y}\\ \end{array} \]

Alternative 5: 63.5% accurate, 11.7× speedup

Math

\[\begin{array}{l} \\ \frac{x}{1 + y \cdot \left(y \cdot 0.16666666666666666\right)} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (/ x (+ 1.0 (* y (* y 0.16666666666666666)))))

C

double code(double x, double y) {
	return x / (1.0 + (y * (y * 0.16666666666666666)));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x / (1.0d0 + (y * (y * 0.16666666666666666d0)))
end function

Java

public static double code(double x, double y) {
	return x / (1.0 + (y * (y * 0.16666666666666666)));
}

Python

def code(x, y):
	return x / (1.0 + (y * (y * 0.16666666666666666)))

Julia

function code(x, y)
	return Float64(x / Float64(1.0 + Float64(y * Float64(y * 0.16666666666666666))))
end

MATLAB

function tmp = code(x, y)
	tmp = x / (1.0 + (y * (y * 0.16666666666666666)));
end

Wolfram

code[x_, y_] := N[(x / N[(1.0 + N[(y * N[(y * 0.16666666666666666), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]
2. Step-by-step derivation

   1. clear-num 99.8%

      \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

   2. un-div-inv 99.8%

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

3. Applied egg-rr 99.8%

   \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

4. Taylor expanded in y around 0 66.3%

   \[\leadsto \frac{x}{\color{blue}{1 + 0.16666666666666666 \cdot {y}^{2}}} \]
5. Step-by-step derivation

   1. *-commutative 66.3%

      \[\leadsto \frac{x}{1 + \color{blue}{{y}^{2} \cdot 0.16666666666666666}} \]

   2. unpow2 66.3%

      \[\leadsto \frac{x}{1 + \color{blue}{\left(y \cdot y\right)} \cdot 0.16666666666666666} \]

6. Simplified 66.3%

   \[\leadsto \frac{x}{\color{blue}{1 + \left(y \cdot y\right) \cdot 0.16666666666666666}} \]

7. Taylor expanded in y around 0 66.3%

   \[\leadsto \frac{x}{1 + \color{blue}{0.16666666666666666 \cdot {y}^{2}}} \]
8. Step-by-step derivation

   1. *-commutative 66.3%

      \[\leadsto \frac{x}{1 + \color{blue}{{y}^{2} \cdot 0.16666666666666666}} \]

   2. unpow2 66.3%

      \[\leadsto \frac{x}{1 + \color{blue}{\left(y \cdot y\right)} \cdot 0.16666666666666666} \]

   3. associate-*l* 66.3%

      \[\leadsto \frac{x}{1 + \color{blue}{y \cdot \left(y \cdot 0.16666666666666666\right)}} \]

9. Simplified 66.3%

   \[\leadsto \frac{x}{1 + \color{blue}{y \cdot \left(y \cdot 0.16666666666666666\right)}} \]

10. Final simplification 66.3%

    \[\leadsto \frac{x}{1 + y \cdot \left(y \cdot 0.16666666666666666\right)} \]

Alternative 6: 56.6% accurate, 14.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.5 \cdot 10^{+38}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= y 1.5e+38) x (* y (/ x y))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.5e+38) {
		tmp = x;
	} else {
		tmp = y * (x / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 1.5d+38) then
        tmp = x
    else
        tmp = y * (x / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.5e+38) {
		tmp = x;
	} else {
		tmp = y * (x / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.5e+38:
		tmp = x
	else:
		tmp = y * (x / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.5e+38)
		tmp = x;
	else
		tmp = Float64(y * Float64(x / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.5e+38)
		tmp = x;
	else
		tmp = y * (x / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.5e+38], x, N[(y * N[(x / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.5000000000000001e38

   1. Initial program 99.9%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in y around 0 67.5%

      \[\leadsto \color{blue}{x} \]

   if 1.5000000000000001e38 < y

   1. Initial program 99.5%

      \[x \cdot \frac{\sin y}{y} \]

   2. Taylor expanded in x around 0 99.6%

      \[\leadsto \color{blue}{\frac{\sin y \cdot x}{y}} \]

   3. Taylor expanded in y around 0 4.0%

      \[\leadsto \frac{\color{blue}{y \cdot x}}{y} \]
   4. Step-by-step derivation

      1. associate-/l* 27.4%

         \[\leadsto \color{blue}{\frac{y}{\frac{y}{x}}} \]

      2. div-inv 27.4%

         \[\leadsto \color{blue}{y \cdot \frac{1}{\frac{y}{x}}} \]

      3. clear-num 24.6%

         \[\leadsto y \cdot \color{blue}{\frac{x}{y}} \]

   5. Applied egg-rr 24.6%

      \[\leadsto \color{blue}{y \cdot \frac{x}{y}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 59.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 1.5 \cdot 10^{+38}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{x}{y}\\ \end{array} \]

Alternative 7: 51.7% accurate, 105.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 x)

C

double code(double x, double y) {
	return x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x
end function

Java

public static double code(double x, double y) {
	return x;
}

Python

def code(x, y):
	return x

Julia

function code(x, y)
	return x
end

MATLAB

function tmp = code(x, y)
	tmp = x;
end

Wolfram

code[x_, y_] := x

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]

2. Taylor expanded in y around 0 55.1%

   \[\leadsto \color{blue}{x} \]

3. Final simplification 55.1%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023199 
(FPCore (x y)
  :name "Linear.Quaternion:$cexp from linear-1.19.1.3"
  :precision binary64
  (* x (/ (sin y) y)))